Several works [1]}, [2]}, [3]} proposed a collection of more than 30 datasets to measure the generalization of weakly / fully-supervised models [4]}, [5]}, [6]}.
Our work builds up on these studies and aims at validating the generalization of our self-supervised trained model on a large set of evaluation tasks.
To this end we use more than 50 computer vision tasks that allow to capture the model's performance on various applications of computer vision.
We argue that measuring model generalization on out-of-domain tasks is important as models can be used “off-the-self” for applications that are hard to anticipate.
